Bojechko Casey, Phillps Mark, Kalet Alan, Ford Eric C
Department of Radiation Oncology, University of Washington, 1959 N. E. Pacific Street, Seattle, Washington 98195.
Med Phys. 2015 Sep;42(9):5363-9. doi: 10.1118/1.4928601.
Complex treatments in radiation therapy require robust verification in order to prevent errors that can adversely affect the patient. For this purpose, the authors estimate the effectiveness of detecting errors with a "defense in depth" system composed of electronic portal imaging device (EPID) based dosimetry and a software-based system composed of rules-based and Bayesian network verifications.
The authors analyzed incidents with a high potential severity score, scored as a 3 or 4 on a 4 point scale, recorded in an in-house voluntary incident reporting system, collected from February 2012 to August 2014. The incidents were categorized into different failure modes. The detectability, defined as the number of incidents that are detectable divided total number of incidents, was calculated for each failure mode.
In total, 343 incidents were used in this study. Of the incidents 67% were related to photon external beam therapy (EBRT). The majority of the EBRT incidents were related to patient positioning and only a small number of these could be detected by EPID dosimetry when performed prior to treatment (6%). A large fraction could be detected by in vivo dosimetry performed during the first fraction (74%). Rules-based and Bayesian network verifications were found to be complimentary to EPID dosimetry, able to detect errors related to patient prescriptions and documentation, and errors unrelated to photon EBRT. Combining all of the verification steps together, 91% of all EBRT incidents could be detected.
This study shows that the defense in depth system is potentially able to detect a large majority of incidents. The most effective EPID-based dosimetry verification is in vivo measurements during the first fraction and is complemented by rules-based and Bayesian network plan checking.
放射治疗中的复杂治疗需要进行严格验证，以防止可能对患者产生不利影响的错误。为此，作者评估了一种“深度防御”系统检测错误的有效性，该系统由基于电子射野影像装置（EPID）的剂量测定法和由基于规则及贝叶斯网络验证组成的软件系统构成。
作者分析了2012年2月至2014年8月期间从内部自愿事件报告系统中记录的、潜在严重程度评分较高（在4分制中评分为3或4）的事件。这些事件被归类为不同的故障模式。计算每种故障模式的可检测性，定义为可检测到的事件数量除以事件总数。
本研究共使用了343起事件。其中67%的事件与光子外照射放疗（EBRT）相关。大多数EBRT事件与患者定位有关，在治疗前进行EPID剂量测定时，只有少数此类事件能够被检测到（6%）。很大一部分事件可通过首次分割时进行的体内剂量测定检测到（74%）。发现基于规则及贝叶斯网络的验证与EPID剂量测定法互补，能够检测与患者处方和记录相关的错误以及与光子EBRT无关的错误。将所有验证步骤结合在一起，所有EBRT事件中有91%能够被检测到。
本研究表明，深度防御系统有可能检测到绝大多数事件。最有效的基于EPID的剂量测定验证是首次分割时的体内测量，并辅以基于规则及贝叶斯网络的计划检查。
